code: def get_scorer_dict(
      suffix: bool = False,
      mode: t.Optional[str] = None,
      scheme: t.Optional[t.Type[Token]] = None,
  ) -> t.Dict[str, t.Callable[[t.List[str], t.List[str]], float]]:
      """Return a dict of scorers for token classification.
  
      Parameters
      ----------
      mode: str, [None (default), `strict`].
          if ``None``, the score is compatible with conlleval.pl. Otherwise,
          the score is calculated strictly.
      scheme: Token, [IOB2, IOE2, IOBES]
      suffix: bool, False by default.
  
      Returns
      -------
          A dict of scorers.
      """
      common_kwargs = {
          "mode": mode,
          "scheme": scheme,
          "suffix": suffix,
          "zero_division": 0,
      }
  
      return {
          "accuracy": make_token_scorer(
              accuracy_score, **common_kwargs
          ),
          "f1_per_class": make_token_scorer(
              f1_score, **common_kwargs, average=None
          ),
          "f1_macro": make_token_scorer(
              f1_score, **common_kwargs, average="macro"
          ),
          "f1_micro": make_token_scorer(
              f1_score, **common_kwargs, average="micro"
          ),
          "precision_per_class": make_token_scorer(
              precision_score, **common_kwargs, average=None
          ),
          "precision_macro": make_token_scorer(
              precision_score, **common_kwargs, average="macro"
          ),
          "precision_micro": make_token_scorer(
              precision_score, **common_kwargs, average="micro"
          ),
          "recall_per_class": make_token_scorer(
              recall_score, **common_kwargs, average=None
          ),
          "recall_macro": make_token_scorer(
              recall_score, **common_kwargs, average="macro"
          ),
          "recall_micro": make_token_scorer(
              recall_score, **common_kwargs, average="micro"
          ),
      }
  
file_path: deepchecks/deepchecks/nlp/metric_utils/token_classification.py
imports: import typing
  from collections.abc import Sequence
  from seqeval.metrics import accuracy_score, f1_score, precision_score, recall_score
  from seqeval.scheme import Token
  from sklearn.metrics import make_scorer
  from deepchecks.core.errors import DeepchecksValueError
label_desc: Use image node_id 1 for calling a global function with example usage: get_scorer_dict(suffix, mode, scheme) and returns: dict